Detection and Diagnosis of Urban Rail Vehicle Auxiliary Inverter Using Wavelet Packet and RBF Neural Network

Guangwu Liu, Jingjing Long, Lingzhi Yang, Z. Su, Dechen Yao, Xiangli Zhong
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引用次数: 1

Abstract

This study concerns with fault diagnosis of urban rail vehicle auxiliary inverter using wavelet packet and RBF neural network. Four statistical features are selected: standard voltage signal, voltage fluctuation signal, impulsive transient signal and frequency variation signal. In this article, the original signals are decomposed into different frequency subbands by wavelet packet. Next, an automatic feature extraction algorithm is constructed. Finally, those wavelet packet energy eigenvectors are taken as fault samples to train RBF neural network. The result shows that the RBF neural network is effective in the detection and diagnosis of various urban rail vehicle auxiliary inverter faults.
基于小波包和RBF神经网络的城市轨道车辆辅助逆变器检测与诊断
研究了基于小波包和RBF神经网络的城市轨道车辆辅助逆变器故障诊断方法。选取四种统计特征:标准电压信号、电压波动信号、脉冲暂态信号和频率变化信号。本文采用小波包将原始信号分解成不同的频率子带。其次,构造了一种自动特征提取算法。最后,将这些小波包能量特征向量作为故障样本,训练RBF神经网络。结果表明,RBF神经网络对各种城市轨道车辆辅助逆变器故障的检测和诊断是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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